Non-iterative Learning Methods

6 June 2018 (Wednesday) 10:30 - 11:30 a.m.

RRS905, Sir Run Run Shaw Building, Ho Sin Hang Campus, HKBU

Abstract
This talk will first introduce the main non-iterative learning paradigms such as the randomization based feedforward neural networks (e.g. RVFL, ELM), random forest, and kernel ridge regression. Some of these non-iterative methods have closed form solutions enabling them to be trained extremely fast. The talk will highlight the similarities and differences among these methods developed over the last 20-25 years. The talk will also present extensive benchmarking studies of these methods using classification and forecasting datasets.

*This Distinguished Lecture is sponsored by the Computational Intelligence Society under its Distinguished Lecturer Program.

Prof. Rama Chellappa

Distinguished University Professor
Minta Martin Professor of Engineering and Chair
Department of Electrical and Computer Engineering
University of Maryland, USA

6 July 2018 (Friday) 4:00 - 5:00 p.m.

LT1 (SCT501), Cha Chi Ming Science Tower, Ho Sin Hang Campus, HKBU

Abstract
Recent developments in deep representation-based methods for many computer vision problems have knocked down many research themes pursued over the last four decades. In this talk, I will discuss methods based on deep representations for designing robust computer vision systems with applications in unconstrained face and action verification and recognition, expression recognition, subject clustering and attribute extraction. The face and action recognition system being built at UMD is based on fusing multiple deep convolutional neural networks (DCNNs) trained using publicly available still and video face data sets and task appropriate loss functions. I will then discuss some new results on generative adversarial learning and domain adaptation for improving the robustness of computer vision systems.

Biography

Pascal Poupart is a Full Professor in the David R. Cheriton School of Computer Science at the University of Waterloo, Waterloo (Canada). He received the B.Sc. in Mathematics and Computer Science at McGill University, Montreal (Canada) in 1998, the M.Sc. in Computer Science at the University of British Columbia, Vancouver (Canada) in 2000 and the Ph.D. in Computer Science at the University of Toronto, Toronto (Canada) in 2005. His research focuses on the development of algorithms for reasoning under uncertainty and machine learning with application to Assistive Technologies, Natural Language Processing and Telecommunication Networks. He is most well known for his contributions to the development of approximate scalable algorithms for partially observable Markov decision processes (POMDPs) and their applications in real-world problems, including automated prompting for people with dementia for the task of handwashing and spoken dialog management. Other notable projects that his research team are currently working on include deep learning with clear semantics, structure learning, personalized transfer learning, conversational agents, adaptive satisfiability and stress detection based on wearable devices.

Pascal Poupart received a Cheriton Faculty Fellowship (2015-2018), a best student paper honourable mention (SAT-2017), an outstanding collaborator award from Huawei Noah's Ark (2016), a top reviewer award (ICML-2016), the best main track solver and best application solver (SAT-2016 competition), a best reviewer award (NIPS-2015), an Early Researcher Award from the Ontario Ministry of Research and Innovation (2008), two Google research awards (2007-2008), a best paper award runner up (UAI-2008) and the IAPR best paper award (ICVS-2007). He also serves as associate editor of the Journal of Artificial Intelligence Research (JAIR) (2017 - present), member of the editorial board of the Journal of Machine Learning Research (JMLR) (2009 - present) and guest editor for Machine Learning Journal (MLJ) (2012 - present). He routinely serves as area chair or senior program committee member for NIPS, ICML, AISTATS, IJCAI, AAAI and UAI. He serves as technical advisor for Huawei Technologies, ElementAI, TalkIQ and ProNavigator. His research collaborators include Huawei Technologies, Google, Intel, Kik Interactive, In the Chat, Slyce, HockeyTech, ProNavigator, the Alzheimer Association, the UW-Schlegel Research Institute for Aging, Sunnybrook Health Science Centre and the Toronto Rehabilitation Institute.

Abstract

In several application domains, data instances are produced by a population of individuals that exhibit a variety of different characteristics. For instance, in activity recognition, different individuals might walk or run with different gait patterns. Similarly, in sleep studies, different individuals might exhibit different patterns for the same sleep stages. In telecommunication networks, software applications might generate packet flows between servers according to different patterns. In such scenarios, it is tempting to treat the population as a homogeneous source of data and to learn a single average model for the entire population. However, this average model will perform poorly in recognition tasks for individuals that differ significantly from the average. Hence, there is a need for transfer learning techniques that take into account the variations between individuals within a population. In this talk, I will describe online algorithms to transfer knowledge on the fly from specific individuals within a population to a new individual in order to bootstrap the learning process in sequential tasks such as activity recognition, sleep stage identification and packet flow prediction in telecommunication networks.

Biography

Joseph A. Konstan is Distinguished McKnight University Professor and Distinguished University Teaching Professor in the Department of Computer Science and Engineering at the University of Minnesota. His research addresses a variety of human-computer interaction issues, including personalization (particularly through recommender systems), eliciting on-line participation, and designing computer systems to improve public health. He is probably best known for his work in collaborative filtering recommenders (the GroupLens project, work which won the ACM Software Systems Award and Seoul Test of Time Award). Dr. Konstan received his Ph.D. from the University of California, Berkeley in 1993. He is a Fellow of the ACM, IEEE, and AAAS, and a member of the CHI Academy. Konstan is co-Chair of the ACM Publications Board, served as President of ACM SIGCHI and is a member of the ACM Council.

Abstract

Recommender systems help users find items of interest and help websites and marketers select items to promote. Today's recommender systems incorporate sophisticated technology to model user preferences, model item properties, and leverage the experiences of a large community of users in the service of better recommendations. Yet all too often better recommendations--at least by traditional measures of accuracy and precision--fail to meet the goal of improving user experience. This talk will take a look at successes and failures in moving beyond basic machine learning approaches to recommender systems to emphasize factors tied to user behavior and experience. Along the way, we will explore a generalizable approach to combining human-centered evaluation with data mining and machine learning techniques.

Biography

Prof. Ikhlaq Sidhu is the founding faculty director and chief scientist of UC Berkeley’s Sutardja Center for Entrepreneurship & Technology. He received the IEOR Emerging Area Professor Award from his department at Berkeley. Ikhlaq Sidhu is an innovator with an industry background and the perspective of an academic.

He teaches, advises, and manages people to enable impactful and relevant innovation, which today demands a different kind of leadership. From him, people learn how to create new things that are technically complex that will also actually have impact in the real world.

He is an innovator in the narrow sense (e.g. created 60+ patents, technology, and products) and he is an innovator in the broad sense (e.g. launched ventures, raised funds, founded organizations, led businesses, and navigated complex organizational challenges) within areas of data networking, telecommunications, and academics.

Sidhu is most known for bringing an industry perspective to academia. He founded the Sutardja Center for Entrepreneurship & Technology in 2005 and launched its many spin-offs programs. With Ken Singer, he co-created the Berkeley Method of Entrepreneurship (for students) and the Berkeley Method of Innovation Leadership (for existing companies). Both of these frameworks add concepts of social-psychology, mindset, and journey to the traditional steps of innovation. The spin-offs from his work at Berkeley include the GVL in 2008, the Fung Institute in 2009, the Engineering Leadership Professional Program in 2011, SkyDeck in 2012, the Innovation Collider in 2015, and Data-X in 2016.

Sidhu serves on several boards and advisory roles including Venture Advisor at Onset Ventures (a leading Silicon Valley investment firm), the Faculty Committee for Lawrence Hall of Science at UC Berkeley, the Board of Trustees of the Hamad Bin Khalifa University, Qatar, Fellow, Applied Innovation Institute, and the Faculty Director’s Council, Jacob’s Institute of Design at UC Berkeley.

Abstract

HKBU has recently embarked upon a journey of integrating the emerging areas of Data Analytics and Artificial Intelligence with "X", where X is effectively a broad range of academic research and teaching areas. Coincidentally, at the University of California Berkeley, we have also been actively developing a new level integration between data related technology areas and a wide array of academic areas including humanities, engineering, and business.

In fact, although Data and AI is an important topic for CS departments in a technical sense, it's also an important teaching and research area for non-technical parts of any university. Topics covered in this talk will include i) research direction examples, ii) strategies to maximize the benefit of Data and AI across the university, and iii) the evolution of data-related education including some examples from recent initiatives at Berkeley and our collaboration with HKBU intended to prepare students for a changing world.

Biography

Edwin R. Hancock holds a BSc degree in physics (1977), a PhD degree in high-energy physics (1981) and a D.Sc. degree (2008) from the University of Durham, and a doctorate Honoris Causa from the University of Alicante in 2015. From 1981-1991 he was at the Rutherford-Appleton Laboratory, working on high energy physics experiments at the Stanford Linear Accelerator Center (SLAC) providing the first measurements of charmed particle lifetimes. In 1991, he moved to the University of York as a lecturer in the Department of Computer Science, where he has held a chair in Computer Vision since 1998. He leads a group of some 25 faculty, research staff, and PhD students working in the areas of computer vision and pattern recognition. He has published about 180 journal papers and 650 refereed conference publications. He was awarded the Pattern Recognition Society medal in 1991 and an outstanding paper award in 1997 by the journal Pattern Recognition. He has also received best paper prizes at CAIP 2001, ACCV 2002, ICPR 2006, BMVC 2007 and ICIAP in 2009 and 2015. In 2009 he was awarded a Royal Society Wolfson Research Merit Award. In 1998, he became a fellow of the International Association for Pattern Recognition. He is also a fellow of the Institute of Physics, the Institute of Engineering and Technology, and the British Computer Society. In 2016 he became a fellow of the IEEE and was named Distinguished Fellow by the British Machine Vision Association. He is currently Editor-in-Chief of the journal Pattern Recognition. He has also been a member of the editorial boards of the journals IEEE Transactions on Pattern Analysis and Machine Intelligence, Pattern Recognition, Computer Vision and Image Understanding, Image and Vision Computing, and the International Journal of Complex Networks. He has been Conference Chair for BMVC in 1994 and Programme Chair in 2016, Track Chair for ICPR in 2004 and 2016 and Area Chair at ECCV 2006 and CVPR in 2008 and 2014, and in 1997 established the EMMCVPR workshop series. He has been a Governing Board Member of the IAPR since 2006, and is currently Vice President of the Association.

Abstract

This talk focuses on how to use network entropy as a means of characterising network structure and investigating the relationship between changes in network structure and function with time. Examples are presented on network data extracted from the data for the New York Stock Exchange. We show how the entropic characterisation can be extended to develop Euler- Lagrange equations which describe the evolution of the node degree distribution, and can be used to predict the evolution of network structure with time. If time permits, we will also describe how to extend our model to include quantum spin statistics, and explore how Bose-Einstein and Fermi-Dirac statistics modify the evolution of network structure. We demonstrate some of the utility of the proposed methods on fMRI images of Alzheimer brains.

Biography

Christian S. Jensen is Obel Professor of Computer Science at Aalborg University, Denmark, and he was recently with Aarhus University for three years and spent a one-year sabbatical at Google Inc., Mountain View. His research concerns data management and data-intensive systems, and its focus is on temporal and spatio-temporal data management. Christian is an ACM and an IEEE Fellow, and he is a member of Academia Europaea, the Royal Danish Academy of Sciences and Letters, and the Danish Academy of Technical Sciences. He has received several national and international awards for his research. He is Editor-in-Chief of ACM Transactions on Database Systems.

Abstract

Important societal processes are increasingly being digitalized. Vehicular transportation is one such process. In the US alone, 112 million people commute by car out of a population of 325 million. The increasing availability of vehicle trajectory data enables the capture of vehicular transportation at an unprecedented level of detail. The speaker argues that in this new setting, the traditional vehicle routing paradigm, Dijkstra’s paradigm, where a road network is modeled as a graph and weights are assigned to edges, is obsolete. Instead, new and data-intensive paradigms that thrive on data are called for. The talk will cover several such paradigms, including a path-based paradigm, an on-the-fly paradigm, and a cost-oblivious paradigm. These paradigms present new challenges and opportunities to research in routing. The talk will cover ongoing research in relation to these paradigms.

Biography

Yaochu Jin received the B.Sc., M.Sc., and Ph.D. degrees from Zhejiang University, Hangzhou, China, in 1988, 1991, and 1996, respectively, and the Dr.-Ing. degree from Ruhr University Bochum, Germany, in 2001.

He is a Professor in Computational Intelligence, Department of Computer Science, University of Surrey, Guildford, U.K., where he heads the Nature Inspired Computing and Engineering Group. His main research interests include evolutionary optimization, machine learning, and evolutionary developmental systems. He has (co)authored over 250 peer-reviewed journal and conference papers and been granted eight patents on evolutionary optimization. He has delivered 30 invited keynote speeches at international conferences.

Dr Jin is the Editor-in-Chief of the IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS and Co-Editor-in-Chief of Complex & Intelligent Systems. He is an IEEE Distinguished Lecturer (2013-2015 and 2017-2019) and past Vice President for Technical Activities of the IEEE Computational Intelligence Society (2014-2015). He is the recipient of the 2018 IEEE Transactions on Evolutionary Computation Outstanding Paper Award, the 2015 and 2017 IEEE Computational Intelligence Magazine Outstanding Paper Award. He is a Fellow of IEEE.

Abstract

Many real-world complex optimization problems can be solved based on data only, which is known as data-driven optimization. In this talk, we discuss the main challenges in data-driven evolutionary algorithms resulting from complexities in data as well the problems to be optimized. We then present recent advances in data-driven optimization that systematically integrate advanced machine learning techniques including active learning, semi-supervised learning and transfer learning, with evolutionary algorithms. Real-world examples are provided to illustrate different model management strategies for handing different data-driven optimization problems.

Biography

Professor Ponnuthurai Nagaratnam Suganthan (or P N Suganthan) received the B.A degree, Postgraduate Certificate and M.A degree in Electrical and Information Engineering from the University of Cambridge, UK in 1990, 1992 and 1994, respectively. After completing his PhD research in 1995, he served as a pre-doctoral Research Assistant in the Department of Electrical Engineering, University of Sydney in 1995–96 and a lecturer in the Department of Computer Science and Electrical Engineering, University of Queensland in 1996–99. He moved to NTU in 1999. He is an Editorial Board Member of the Evolutionary Computation Journal, MIT Press. He is an associate editor of the IEEE Trans on Cybernetics (2012 - ), IEEE Trans on Evolutionary Computation (2005 -), Information Sciences (Elsevier) (2009 - ), Pattern Recognition (Elsevier) (2001 - ) and Int. J. of Swarm Intelligence Research (2009 - ) Journals. He is a founding co-editor-in-chief of Swarm and Evolutionary Computation (2010 - ), an SCI Indexed Elsevier Journal. His co-authored SaDE paper (published in April 2009) won the "IEEE Trans. on Evolutionary Computation outstanding paper award" in 2012. His former PhD student, Dr Jane Jing Liang, won the IEEE CIS Outstanding PhD dissertation award, in 2014. His research interests include swarm and evolutionary algorithms, pattern recognition, big data, deep learning and applications of swarm, evolutionary & machine learning algorithms. He was selected as one of the highly cited researchers by Thomson Reuters in 2015, 2016 and 2017 in computer science. He served as the General Chair of the IEEE SSCI 2013. He has been a member of the IEEE since 1990 and Fellow since 2015. He was an elected AdCom member of the IEEE Computational Intelligence Society (CIS) in 2014-2016.

Abstract

This talk will first introduce the main non-iterative learning paradigms such as the randomization based feedforward neural networks (e.g. RVFL, ELM), random forest, and kernel ridge regression. Some of these non-iterative methods have closed form solutions enabling them to be trained extremely fast. The talk will highlight the similarities and differences among these methods developed over the last 20-25 years. The talk will also present extensive benchmarking studies of these methods using classification and forecasting datasets.

Biography

Prof. Rama Chellappa is a Distinguished University Professor, a Minta Martin Professor of Engineering and Chair of the ECE department at the University of Maryland. His current research interests span many areas in image processing, computer vision, machine learning and pattern recognition. Prof. Chellappa is a recipient of an NSF Presidential Young Investigator Award and four IBM Faculty Development Awards. He received the K.S. Fu Prize from the International Association of Pattern Recognition (IAPR). He is a recipient of the Society, Technical Achievement and Meritorious Service Awards from the IEEE Signal Processing Society. He also received the Technical Achievement and Meritorious Service Awards from the IEEE Computer Society. Recently, he received the inaugural Leadership Award from the IEEE Biometrics Council. At UMD, he received college and university level recognitions for research, teaching, innovation and mentoring of undergraduate students. In 2010, he was recognized as an Outstanding ECE by Purdue University. He received the Distinguished Alumni Award from the Indian Institute of Science in 2016. Prof. Chellappa served as the Editor-in-Chief of PAMI. He is a Golden Core Member of the IEEE Computer Society, served as a Distinguished Lecturer of the IEEE Signal Processing Society and as the President of IEEE Biometrics Council. He is a Fellow of IEEE, IAPR, OSA, AAAS, ACM and AAAI and holds six patents.

Abstract

Recent developments in deep representation-based methods for many computer vision problems have knocked down many research themes pursued over the last four decades. In this talk, I will discuss methods based on deep representations for designing robust computer vision systems with applications in unconstrained face and action verification and recognition, expression recognition, subject clustering and attribute extraction. The face and action recognition system being built at UMD is based on fusing multiple deep convolutional neural networks (DCNNs) trained using publicly available still and video face data sets and task appropriate loss functions. I will then discuss some new results on generative adversarial learning and domain adaptation for improving the robustness of computer vision systems.

Past Events

Speaker

Title

Date & Venue

Prof. Pascal PoupartProfessor
David R. Cheriton School of Computer Science, University of Waterloo, Canada